Chapter 02

Understanding Market Data

Before you can trade with data, you need to understand what data exists, what it tells you, and how to read it. This chapter breaks down the most important types of market data that quant traders rely on.

Price Data (OHLCV)

The foundation of all trading analysis. Every candle on a chart represents four prices and one volume figure:

  • Open: The first price traded in that period
  • High: The highest price during the period
  • Low: The lowest price during the period
  • Close: The last price traded — the most important for analysis
  • Volume: Total shares/contracts traded — measures participation
A candle with a high close relative to its range (close near the high) shows buying pressure. A close near the low shows selling pressure.

Timeframes Matter

The same stock can look bullish on a 5-minute chart and bearish on a daily chart. Quant traders choose timeframes based on their strategy:

  • Tick / 1-second: High-frequency trading, market making
  • 1-minute / 5-minute: Day trading, scalping
  • 15-min / 1-hour: Intraday swing trades
  • Daily: Swing trading, the most common for quant strategies
  • Weekly / Monthly: Position trading, macro strategies

Rule of thumb: longer timeframes have more reliable signals but fewer trading opportunities. Shorter timeframes offer more trades but more noise.

Volume: The Truth Detector

Price tells you what happened. Volume tells you how much conviction was behind it. Key volume concepts:

  • High volume breakout: A price move on unusually high volume is more likely to continue. Big players are committing capital.
  • Low volume drift: A price move on thin volume is weak and likely to reverse. Nobody is really behind it.
  • Volume climax: A sudden spike in volume often marks exhaustion — the last buyers or sellers rushing in before a reversal.
  • Relative volume (RVOL): Today's volume compared to average volume. An RVOL of 3x means three times normal activity — something is happening.

Order Flow & the Order Book

While price and volume show you what already happened, order flow shows you what's about to happen. The order book displays:

  • Bids: Buy orders waiting at various price levels (demand)
  • Asks: Sell orders waiting at various price levels (supply)
  • Spread: The gap between the best bid and best ask — tighter spread means more liquid
  • Depth: How many orders are stacked at each level — reveals support and resistance

Large institutional orders show up in the flow data. When you see consistent large buys hitting the ask (aggressive buying), smart money is accumulating. The opposite signals distribution.

Volatility Data

Volatility measures how much prices are swinging. It's not direction — it's magnitude.

  • Historical volatility: Calculated from past price movements. A stock averaging 2% daily moves is more volatile than one averaging 0.5%.
  • Implied volatility (IV): Derived from options prices. High IV means the market expects big moves ahead (earnings, catalysts).
  • VIX: The “fear index” — implied volatility of S&P 500 options. VIX above 30 = high fear. Below 15 = complacency.
Volatility clusters: periods of high volatility tend to follow other periods of high volatility. Calm markets eventually explode, and volatile markets eventually calm down.

Sentiment Data

Sentiment measures what market participants are feeling, which often acts as a contrarian indicator:

  • Put/Call ratio: High ratio = bearish sentiment. Extreme readings often precede reversals.
  • Short interest: How much of a stock is sold short. Extremely high short interest can fuel short squeezes.
  • AAII survey: Retail investor sentiment. Extreme bullishness or bearishness tends to mean-revert.
  • Dark pool activity: Large institutional trades executed off-exchange — reveals where big money flows.

Where to Get Market Data

  • Free: Yahoo Finance, Google Finance, FRED (economic data), TradingView (charts)
  • Broker platforms: Most brokers provide real-time data — Thinkorswim, Interactive Brokers, Webull
  • Paid data: Quandl, Polygon.io, Alpha Vantage — for historical and alternative data
  • Institutional: Bloomberg Terminal, Refinitiv — expensive but comprehensive

In the next chapter, we'll take this raw data and turn it into actionable trading signals.